

"""simple docstring"""




import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


__UpperCAmelCase			=					logging.get_logger(__name__)







class   __lowercase      (     __lowerCamelCase       ):
  snake_case_												=							"""linear"""
  snake_case_												=							"""cosine"""
  snake_case_												=							"""cosine_with_restarts"""
  snake_case_												=							"""polynomial"""
  snake_case_												=							"""constant"""
  snake_case_												=							"""constant_with_warmup"""
  snake_case_												=							"""piecewise_constant"""







def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase = -1   ):
 '''simple docstring'''





 return LambdaLR(__UpperCamelCase				,     lambda __UpperCamelCase   : 1				,     last_epoch=__UpperCamelCase   )
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase = -1   ):
 '''simple docstring'''





 def lr_lambda(__UpperCamelCase   ):
  if current_step < num_warmup_steps:
   return float(__UpperCamelCase   ) / float(max(1.0				,     __UpperCamelCase   )   )
  return 1.0

 return LambdaLR(__UpperCamelCase				,     __UpperCamelCase				,     last_epoch=__UpperCamelCase   )
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase = -1   ):
 '''simple docstring'''





 UpperCAmelCase__       :	Dict        =       {}
 UpperCAmelCase__       :	Optional[int]        =       step_rules.split(""","""   )
 for rule_str in rule_list[:-1]:
  UpperCAmelCase__ ,   UpperCAmelCase__       :	int        =       rule_str.split(""":"""   )
  UpperCAmelCase__       :	Any        =       int(__UpperCamelCase   )
  UpperCAmelCase__       :	Dict        =       float(__UpperCamelCase   )
  UpperCAmelCase__       :	int        =       value
 UpperCAmelCase__       :	Dict        =       float(rule_list[-1]   )

 def create_rules_function(__UpperCamelCase				,     __UpperCamelCase   ):
  def rule_func(__UpperCamelCase   ) -> float:
   UpperCAmelCase__       :	Optional[Any]        =       sorted(rules_dict.keys()   )
   for i, sorted_step in enumerate(__UpperCamelCase   ):
    if steps < sorted_step:
     return rules_dict[sorted_steps[i]]
   return last_lr_multiple

  return rule_func

 UpperCAmelCase__       :	Dict        =       create_rules_function(__UpperCamelCase				,     __UpperCamelCase   )

 return LambdaLR(__UpperCamelCase				,     __UpperCamelCase				,     last_epoch=__UpperCamelCase   )
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase=-1   ):
 '''simple docstring'''





 def lr_lambda(__UpperCamelCase   ):
  if current_step < num_warmup_steps:
   return float(__UpperCamelCase   ) / float(max(1				,     __UpperCamelCase   )   )
  return max(
      0.0				,     float(num_training_steps - current_step   ) / float(max(1				,     num_training_steps - num_warmup_steps   )   )   )

 return LambdaLR(__UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase   )
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase = 0.5				,     __UpperCamelCase = -1   ):
 '''simple docstring'''





 def lr_lambda(__UpperCamelCase   ):
  if current_step < num_warmup_steps:
   return float(__UpperCamelCase   ) / float(max(1				,     __UpperCamelCase   )   )
  UpperCAmelCase__       :	Optional[Any]        =       float(current_step - num_warmup_steps   ) / float(max(1				,     num_training_steps - num_warmup_steps   )   )
  return max(0.0				,     0.5 * (1.0 + math.cos(math.pi * float(__UpperCamelCase   ) * 2.0 * progress   ))   )

 return LambdaLR(__UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase   )
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase = 1				,     __UpperCamelCase = -1   ):
 '''simple docstring'''





 def lr_lambda(__UpperCamelCase   ):
  if current_step < num_warmup_steps:
   return float(__UpperCamelCase   ) / float(max(1				,     __UpperCamelCase   )   )
  UpperCAmelCase__       :	Optional[Any]        =       float(current_step - num_warmup_steps   ) / float(max(1				,     num_training_steps - num_warmup_steps   )   )
  if progress >= 1.0:
   return 0.0
  return max(0.0				,     0.5 * (1.0 + math.cos(math.pi * ((float(__UpperCamelCase   ) * progress) % 1.0)   ))   )

 return LambdaLR(__UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase   )
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase=1e-7				,     __UpperCamelCase=1.0				,     __UpperCamelCase=-1   ):
 '''simple docstring'''





 UpperCAmelCase__       :	List[Any]        =       optimizer.defaults["""lr"""]
 if not (lr_init > lr_end):
  raise ValueError(F"lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"   )

 def lr_lambda(__UpperCamelCase   ):
  if current_step < num_warmup_steps:
   return float(__UpperCamelCase   ) / float(max(1				,     __UpperCamelCase   )   )
  elif current_step > num_training_steps:
   return lr_end / lr_init  # as LambdaLR multiplies by lr_init
  else:
   UpperCAmelCase__       :	List[str]        =       lr_init - lr_end
   UpperCAmelCase__       :	List[Any]        =       num_training_steps - num_warmup_steps
   UpperCAmelCase__       :	Union[str, Any]        =       1 - (current_step - num_warmup_steps) / decay_steps
   UpperCAmelCase__       :	Any        =       lr_range * pct_remaining**power + lr_end
   return decay / lr_init  # as LambdaLR multiplies by lr_init

 return LambdaLR(__UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase   )


__UpperCAmelCase			=					{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}




def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase = None				,     __UpperCamelCase = None				,     __UpperCamelCase = None				,     __UpperCamelCase = 1				,     __UpperCamelCase = 1.0				,     __UpperCamelCase = -1				,     ):
 '''simple docstring'''





 UpperCAmelCase__       :	List[str]        =       SchedulerType(__UpperCamelCase   )
 UpperCAmelCase__       :	Optional[Any]        =       TYPE_TO_SCHEDULER_FUNCTION[name]
 if name == SchedulerType.CONSTANT:
  return schedule_func(__UpperCamelCase				,     last_epoch=__UpperCamelCase   )

 if name == SchedulerType.PIECEWISE_CONSTANT:
  return schedule_func(__UpperCamelCase				,     step_rules=__UpperCamelCase				,     last_epoch=__UpperCamelCase   )

 # All other schedulers require `num_warmup_steps`
 if num_warmup_steps is None:
  raise ValueError(F"{name} requires `num_warmup_steps`, please provide that argument."   )

 if name == SchedulerType.CONSTANT_WITH_WARMUP:
  return schedule_func(__UpperCamelCase				,     num_warmup_steps=__UpperCamelCase				,     last_epoch=__UpperCamelCase   )

 # All other schedulers require `num_training_steps`
 if num_training_steps is None:
  raise ValueError(F"{name} requires `num_training_steps`, please provide that argument."   )

 if name == SchedulerType.COSINE_WITH_RESTARTS:
  return schedule_func(
      __UpperCamelCase				,     num_warmup_steps=__UpperCamelCase				,     num_training_steps=__UpperCamelCase				,     num_cycles=__UpperCamelCase				,     last_epoch=__UpperCamelCase				,     )

 if name == SchedulerType.POLYNOMIAL:
  return schedule_func(
      __UpperCamelCase				,     num_warmup_steps=__UpperCamelCase				,     num_training_steps=__UpperCamelCase				,     power=__UpperCamelCase				,     last_epoch=__UpperCamelCase				,     )

 return schedule_func(
     __UpperCamelCase				,     num_warmup_steps=__UpperCamelCase				,     num_training_steps=__UpperCamelCase				,     last_epoch=__UpperCamelCase   )












'''simple docstring'''






def   __A    (				a_    :	list[list[float]]						):
		lowerCAmelCase  :     list[list[float]]       						= []
		for data in source_data:
				for i, el in enumerate(a_						):
						if len(a_						) < i + 1:
								data_lists.append([]						)
						data_lists[i].append(float(a_						)						)
		return data_lists






def   __A    (				a_    :	list[list[float]]   ,a_    :	list[int]						):
		lowerCAmelCase  :     list[list[float]]       						= []
		for dlist, weight in zip(a_   ,a_						):
				lowerCAmelCase  :     Optional[Any]       						= min(a_						)
				lowerCAmelCase  :     List[str]       						= max(a_						)

				lowerCAmelCase  :     list[float]       						= []
				# for weight 0 score is 1 - actual score
				if weight == 0:
						for item in dlist:
								try:
										score.append(1 - ((item - mind) / (maxd - mind))						)
								except ZeroDivisionError:
										score.append(1						)

				elif weight == 1:
						for item in dlist:
								try:
										score.append((item - mind) / (maxd - mind)						)
								except ZeroDivisionError:
										score.append(0						)

        # weight not 0 or 1
				else:
						lowerCAmelCase  :     List[Any]       						= f'''Invalid weight of {weight:f} provided'''
						raise ValueError(a_						)

				score_lists.append(a_						)

		return score_lists






def   __A    (				a_    :	list[list[float]]						):
		lowerCAmelCase  :     list[float]       						= [0 for i in range(len(score_lists[0]						)						)]

		for slist in score_lists:
				for j, ele in enumerate(a_						):
						lowerCAmelCase  :     Optional[Any]       						= final_scores[j] + ele

		return final_scores






def   __A    (				a_    :	list[list[float]]   ,a_    :	list[int]						):
		lowerCAmelCase  :     Union[str, Any]       						= get_data(a_						)
		lowerCAmelCase  :     List[Any]       						= calculate_each_score(a_   ,a_						)
		lowerCAmelCase  :     Optional[Any]       						= generate_final_scores(a_						)

		# append scores to source data
		for i, ele in enumerate(a_						):
				source_data[i].append(a_						)

		return source_data



